Recurrent neuro-fuzzy model of pneumatic artificial muscle position


In this paper, a dynamic neuro-fuzzy system is proposed toward modeling the pneumatic artificial muscle, which are widely used in robotics and rehabilitation. To benefit from the outstanding advantages of the pneumatic actuators such as high softness and low weight-to-force ratio, efficient control of the actuator force as well as its displacement is essential. Attaining a comprehensive model with a satisfactory accuracy in the entire course of the muscle is the most important challenge regarding utilization of the pneumatic artificial muscle in a wide range of the applications. Therefore, an adaptive neuro-fuzzy inference system has been developed for pneumatic artificial muscle modeling. The subtractive clustering method is applied to reduce the number of fuzzy rules without loss of accuracy. To verify the effectiveness of the proposed modeling approach, an experimental setup has been constructed using a vertically suited pneumatic artificial muscle which holds a mass. Input-output data are collected for training and testing the recurrent neuro-fuzzy model. The experimental results demonstrate the desirable performance of the proposed adaptive neuro-fuzzy inference system method in modeling the pneumatic artificial muscle as well as its superiority compared to the mathematical model.

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The authors would like to thank Mr. Nima Zamani Meymian from West Virginia University for his support and valuable comments during the course of this research that greatly helped us to enhance the quality of the paper.

Author information

Correspondence to Mostafa Taghizadeh.

Additional information

Recommended by Editor Sehyun Shin

Mahdi Chavoshian received the B.Sc. degree in mechanical engineering from Shahrood University of Technology, Iran in 2008, and the M.Sc. degree in mechanical engineering from Islamic Azad University South Tehran Branch, Iran in 2011. Currently, he is a Ph.D. degree candidate in the Department of Mechanical and Energy Engineering at Shahid Beheshti University, Iran. His research interests include dynamic systems, feedback control systems, specially modeling and control of servo pneumatic systems.

Mostafa Taghizadeh received the B.Sc. and M.Sc. degrees in mechanical engineering from the University of Tehran, Iran in 1995 and 1997, respectively, and the Ph.D. degree in mechanical engineering from K. N. Toosi University of Technology, Iran in 2008. Currently, he is an Assistant Professor in the Department of Mechanical and Energy Engineering at Shahid Beheshti University, Iran. His research interests include fluid power control systems, robotics and feedback control systems.

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Chavoshian, M., Taghizadeh, M. Recurrent neuro-fuzzy model of pneumatic artificial muscle position. J Mech Sci Technol 34, 499–508 (2020).

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  • Pneumatic artificial muscle
  • Neuro-fuzzy modeling
  • Subtractive clustering
  • Analytical model